Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization
Authors: Runqi Lin, Chaojian Yu, Tongliang Liu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our method can effectively eliminate CO and further boost adversarial robustness with negligible additional computational overhead. In this section, we provide a comprehensive evaluation to verify the effectiveness of AAER, including experiment settings (Section 4.1), performance evaluation (Section 4.2), ablation studies (Section 4.3) and time complexity study (Section 4.4). |
| Researcher Affiliation | Academia | Runqi Lin Chaojian Yu Tongliang Liu Sydney AI Centre, The University of Sydney EMAIL |
| Pseudocode | Yes | Algorithm 1 Abnormal Adversarial Examples Regularization (AAER) |
| Open Source Code | Yes | Our implementation can be found at https://github.com/tmllab/2023_NeurIPS_AAER. |
| Open Datasets | Yes | We evaluate our method on several benchmark datasets, including Cifar-10/100 [22], SVHN [28], Tiny-Image Net [28] and Imagenet-100 [7]. |
| Dataset Splits | No | No explicit statement specifying training/validation/test split percentages or sample counts, or direct citations to specific split methodologies was found for reproducibility. |
| Hardware Specification | Yes | Table 4. CIFAR10 training time on a single NVIDIA RTX 4090 GPU using Preact Res Net-18. |
| Software Dependencies | No | No explicit listing of software dependencies with specific version numbers (e.g., Python 3.8, PyTorch 1.9) was found. |
| Experiment Setup | Yes | In this work, we use the SGD optimizer with a momentum of 0.9, weight decay of 5 Ć 10ā4 and Lā as the threat model. For the learning rate schedule, we use the cyclical learning rate schedule [32] with 30 epochs, which reaches its maximum learning rate (0.2) when half of the epochs (15) are passed. The hyperparameter settings for Cifar-10/100 are summarized in the Table 1. |